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Paper Citation Record · LEDGER

Dual Feature Decoupling for Fine-Grained OOD Detection

As of 11 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2606.05536.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.05536 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T03:07:59.018717Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

  • verified exact9
  • verified fuzzy0
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d525561-531d-459f-9e15-c266e0b625ee · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,.

Dual Feature Decoupling for Fine-Grained OOD Detection Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,

Reference 1

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Observation 5c443be4-75cc-439b-aa6c-bff4c60659f9 · outbound

This paper cites A baseline for detecting misclassified and out-of-distribution examples in neural networks,.

Dual Feature Decoupling for Fine-Grained OOD Detection A baseline for detecting misclassified and out-of-distribution examples in neural networks,

Reference 2

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Observation 7e4bd243-38b7-475d-9311-fd80ce702862 · outbound

This paper cites Mood: Multi-level out-of-distribution detection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Mood: Multi-level out-of-distribution detection,

Reference 3

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Observation 564ba0d1-9f88-413a-93a3-b33c327d576a · outbound

This paper cites Mitigating neural network overconfidence with logit normalization,.

Dual Feature Decoupling for Fine-Grained OOD Detection Mitigating neural network overconfidence with logit normalization,

Reference 4

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Observation 54d480c3-ea2e-4c92-9730-f7503a26ccb4 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks,.

Dual Feature Decoupling for Fine-Grained OOD Detection A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

Reference 5

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Observation 3b2c96a6-efd6-45c2-82d6-46c004d5d52a · outbound

This paper cites Uncertainty estima- tion using a single deep deterministic neural network,.

Dual Feature Decoupling for Fine-Grained OOD Detection Uncertainty estima- tion using a single deep deterministic neural network,

Reference 6

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Observation 9415acbb-6340-47d9-8c35-fdea14f23eb9 · outbound

This paper cites Detecting out-of-distribution examples with gram matrices,.

Dual Feature Decoupling for Fine-Grained OOD Detection Detecting out-of-distribution examples with gram matrices,

Reference 7

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Observation 0d203824-e69b-478b-8e9c-a207b0459fcd · outbound

This paper cites Learning multiple layers of features from tiny images,.

Dual Feature Decoupling for Fine-Grained OOD Detection Learning multiple layers of features from tiny images,

Reference 8

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Observation 19a53bd5-3645-4469-8da3-92813931da32 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

Dual Feature Decoupling for Fine-Grained OOD Detection LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 9

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Observation c1545cfa-8b30-4f97-b8cf-bcf9fe446416 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

Dual Feature Decoupling for Fine-Grained OOD Detection Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 10

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arxiv_id, observed 2026-07-02T11:46:55.207346Z

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Observation c13027b9-8a1c-4487-8f06-3b1e82ec6dd8 · outbound

This paper cites Likelihood regret: An out-of-distribution detection score for variational auto-encoder,.

Dual Feature Decoupling for Fine-Grained OOD Detection Likelihood regret: An out-of-distribution detection score for variational auto-encoder,

Reference 11

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Observation 10db34a5-9537-491b-b25e-f28022b14c8a · outbound

This paper cites Out-of-distribution detection using union of 1- dimensional subspaces,.

Dual Feature Decoupling for Fine-Grained OOD Detection Out-of-distribution detection using union of 1- dimensional subspaces,

Reference 12

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Observation 84e06382-a689-4b95-bf46-7eb37f4c2f75 · outbound

This paper cites A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection.

Dual Feature Decoupling for Fine-Grained OOD Detection A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection

Reference 13

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Observation ec58decc-daca-4535-8b35-fb51e29fa26f · outbound

This paper cites Vim: Out-of-distribution with virtual-logit matching,.

Dual Feature Decoupling for Fine-Grained OOD Detection Vim: Out-of-distribution with virtual-logit matching,

Reference 14

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Observation 4dde8f6a-4469-45f3-987d-85710701eb9e · outbound

This paper cites Leveraging perturbation robustness to enhance out-of-distribution detection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Leveraging perturbation robustness to enhance out-of-distribution detection,

Reference 15

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Observation 602e6942-188d-4246-bbfc-c94ccaa32532 · outbound

This paper cites Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments,.

Dual Feature Decoupling for Fine-Grained OOD Detection Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments,

Reference 16

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Observation c405f7a3-0cfc-49fa-8bdd-69c5a9cf17a6 · outbound

This paper cites Energy-based out-of-distribution detection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Energy-based out-of-distribution detection,

Reference 17

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Observation 9acaf857-7ddb-4c4f-9f5e-ecb53dedc5f7 · outbound

This paper cites Decoupling maxlogit for out-of-distribution detection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Decoupling maxlogit for out-of-distribution detection,

Reference 18

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Observation ba39177a-4820-451b-b735-4329dadb8565 · outbound

This paper cites Sle: Out-of-distribution detection with shallow layer-driven enhancement,.

Dual Feature Decoupling for Fine-Grained OOD Detection Sle: Out-of-distribution detection with shallow layer-driven enhancement,

Reference 19

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Observation e0f1c8fb-a17c-4917-b1ce-05bfed57bbb1 · outbound

This paper cites Multimodal classification and out-of-distribution detection for multimodal intent understanding,.

Dual Feature Decoupling for Fine-Grained OOD Detection Multimodal classification and out-of-distribution detection for multimodal intent understanding,

Reference 20

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Observation 772a7ae4-92d3-47f7-8c2c-4d68f6d1fe71 · outbound

This paper cites Class incremental learning for image classification with out-of-distribution task identification,.

Dual Feature Decoupling for Fine-Grained OOD Detection Class incremental learning for image classification with out-of-distribution task identification,

Reference 21

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Observation ca71af62-09f1-4efb-85fe-bbbcef4cefbd · outbound

This paper cites Learning confidence for out-of- distribution detection in neural networks,.

Dual Feature Decoupling for Fine-Grained OOD Detection Learning confidence for out-of- distribution detection in neural networks,

Reference 22

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Observation 6fcd11a6-4406-45a4-936f-662d2690b30f · outbound

This paper cites Musia: Exploiting multi-source information fusion with abnormal activations for out-of-distribution detection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Musia: Exploiting multi-source information fusion with abnormal activations for out-of-distribution detection,

Reference 23

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Observation 3bad19bb-7cdc-4362-a165-16367e15ddb1 · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

Dual Feature Decoupling for Fine-Grained OOD Detection AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 24

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Observation cac33eff-19b9-42a9-b4a3-96548dac17d9 · outbound

This paper cites Certifiably adversarially robust detection of out-of-distribution data,.

Dual Feature Decoupling for Fine-Grained OOD Detection Certifiably adversarially robust detection of out-of-distribution data,

Reference 25

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Observation be4558b8-0971-4902-8b87-9fc39ab3222a · outbound

This paper cites On mixup training: Improved calibration and predictive uncertainty for deep neural networks,.

Dual Feature Decoupling for Fine-Grained OOD Detection On mixup training: Improved calibration and predictive uncertainty for deep neural networks,

Reference 26

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Observation d4d60bc2-e22e-4ce7-9553-ff7e87323466 · outbound

This paper cites Deep Anomaly Detection with Outlier Exposure.

Dual Feature Decoupling for Fine-Grained OOD Detection Deep Anomaly Detection with Outlier Exposure

Reference 27

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Observation 445084fc-346c-4cc6-a4c0-70a76d44748d · outbound

This paper cites Generalized outlier exposure: Towards a trustworthy out-of-distribution detector without sacrificing accuracy,.

Dual Feature Decoupling for Fine-Grained OOD Detection Generalized outlier exposure: Towards a trustworthy out-of-distribution detector without sacrificing accuracy,

Reference 28

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Observation 774d1cd3-ad2c-4129-9709-011c26b3c19d · outbound

This paper cites Atom: Robustifying out- of-distribution detection using outlier mining,.

Dual Feature Decoupling for Fine-Grained OOD Detection Atom: Robustifying out- of-distribution detection using outlier mining,

Reference 29

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Observation 8479f297-8874-48bc-af56-57120504cdf9 · outbound

This paper cites Domain agnostic learn- ing with disentangled representations,.

Dual Feature Decoupling for Fine-Grained OOD Detection Domain agnostic learn- ing with disentangled representations,

Reference 30

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Observation 520c6032-d1ff-4bdb-8ae7-b09fdbaad82e · outbound

This paper cites Disentangled representation for age- invariant face recognition: A mutual information minimization perspec- tive,.

Dual Feature Decoupling for Fine-Grained OOD Detection Disentangled representation for age- invariant face recognition: A mutual information minimization perspec- tive,

Reference 31

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Observation 26935300-584e-4e41-907a-e7d9dd44e6f4 · outbound

This paper cites Decompose, adjust, compose: Effective normalization by playing with frequency for domain generalization,.

Dual Feature Decoupling for Fine-Grained OOD Detection Decompose, adjust, compose: Effective normalization by playing with frequency for domain generalization,

Reference 32

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Observation 1fc136fe-7b53-4a6f-80db-afd8327c6895 · outbound

This paper cites Crada: Cross domain object detection with cyclic reconstruction and decoupling adaptation,.

Dual Feature Decoupling for Fine-Grained OOD Detection Crada: Cross domain object detection with cyclic reconstruction and decoupling adaptation,

Reference 33

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Observation 369c1b0c-70b8-4c6d-8f0c-1dd1005f47da · outbound

This paper cites Multi-layer decoupling attention network for weakly supervised object localization,.

Dual Feature Decoupling for Fine-Grained OOD Detection Multi-layer decoupling attention network for weakly supervised object localization,

Reference 34

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Observation 62ce0fee-958d-445a-922e-eb38633fa77f · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Dual Feature Decoupling for Fine-Grained OOD Detection Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 35

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source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:88850f2e49aa76a0c143740aec43d6b2b8ba4d49b756543d4d58703b5b2ec764

Observation 1b8e60a8-a824-4560-8f19-9641ac9ad1f0 · outbound

This paper cites Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis,.

Dual Feature Decoupling for Fine-Grained OOD Detection Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis,

Reference 36

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Observation dd53dc71-81e4-4b87-99d4-7c1171acb4b6 · outbound

This paper cites Learning deep features for discriminative localization,.

Dual Feature Decoupling for Fine-Grained OOD Detection Learning deep features for discriminative localization,

Reference 37

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Observation 2babbe9c-96db-46c4-8dfb-65ba06c981e0 · outbound

This paper cites Fine-Grained Visual Classification of Aircraft.

Dual Feature Decoupling for Fine-Grained OOD Detection Fine-Grained Visual Classification of Aircraft

Reference 38

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verified exact
local_arxiv, observed 2026-07-02T11:46:55.186904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 46029167-8373-43df-83a7-563f3c613290 · outbound

This paper cites 3d object representations for fine-grained categorization,.

Dual Feature Decoupling for Fine-Grained OOD Detection 3d object representations for fine-grained categorization,

Reference 39

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unresolved
no resolver link, observed 2026-06-28T03:07:59.018717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2505499a-12d3-419c-96fe-894b8c724957 · outbound

This paper cites Fine-grained representation learning and recognition by exploiting hierarchical semantic embedding,.

Dual Feature Decoupling for Fine-Grained OOD Detection Fine-grained representation learning and recognition by exploiting hierarchical semantic embedding,

Reference 40

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verified exact
arxiv_id, observed 2026-06-28T03:11:30.033166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation f717ed1c-2e4a-4cb9-9a14-4b89c160eb20 · outbound

This paper cites Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,.

Dual Feature Decoupling for Fine-Grained OOD Detection Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection,

Reference 41

Resolution
unresolved
no resolver link, observed 2026-06-28T03:07:59.018717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:46e63ca3526a9f307f62fb753320f1a53df505c86e1d932de24f5a52246c5a57

Observation 45537884-f942-4ef8-8790-c8f4fbb4a043 · outbound

This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

Dual Feature Decoupling for Fine-Grained OOD Detection WebVision Database: Visual Learning and Understanding from Web Data

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-07-02T11:46:55.180956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:163e07e94a9ab4862f2a504e15983600623e99ff8c8258199fa68dcb653dba6f

Observation 52e3d6d8-0cf6-4d35-b1f7-15fe31a1a9b7 · outbound

This paper cites Detecting semantic anomalies,.

Dual Feature Decoupling for Fine-Grained OOD Detection Detecting semantic anomalies,

Reference 43

Resolution
unresolved
no resolver link, observed 2026-06-28T03:07:59.018717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:7accff4971b6a57cc6d67841f8298e4b5cfd42017eddccb19e5a384608291b65

Observation 9fe7025c-5ef7-43e0-9ab5-e00a1be88895 · outbound

This paper cites Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement.

Dual Feature Decoupling for Fine-Grained OOD Detection Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:46:55.200902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:ee7ae8d0dc7a1e1c6f67368ef9d088d6fa026d619bd260b10b6f27bf561748ac

Observation ccd2cecc-da78-496f-b294-e8e43fc49e19 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Dual Feature Decoupling for Fine-Grained OOD Detection SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-07-02T11:46:55.193963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-28T03:07:59.018717Z digest=sha256:f6fb5b74b8ee0eda471dc80c943b10a417c529e942025370e509e22070ceadc3

Pith citing papers

No inbound Pith citation observations are available.